AI Glossary for International Education
As Artificial Intelligence (AI) continues to transform global education, understanding key AI-related terminology and concepts is essential for educators, administrators, and students. This glossary offers a comprehensive overview of the fundamental terms and broader concepts that shape AI’s role in international higher education.
It is divided into two sections: AI Terms and AI Concepts. AI Terms focus on specific technologies and systems that are driving innovation in education, while AI Concepts explore the broader ideas and applications of AI, highlighting how these technologies are reshaping the educational landscape.
Whether you're new to AI or looking to deepen your understanding, this glossary will provide you with a solid foundation to engage with AI in the context of international higher education.
Adaptive Learning Systems
T
E
R
M
S
AI-powered platforms that customize educational content based on students’ individual learning needs and preferences. These systems are key in international higher education, offering tailored support to students from diverse educational backgrounds.
Artificial Intelligence (AI)
T
E
R
M
S
The simulation of human intelligence in machines designed to think and act like humans. AI encompasses techniques such as machine learning and deep learning, enabling machines to perform complex tasks and learn from data without explicit programming. In international higher education, AI supports areas like student recruitment, personalized learning, and administrative automation.
Artificial General Intelligence (AGI)
T
E
R
M
S
A theoretical form of AI that could perform any intellectual task that a human being can. AGI is a long-term goal of AI research, but discussions around its ethical and societal implications are already influencing AI governance in education.
Artificial Superintelligence (ASI)
T
E
R
M
S
A hypothetical future form of AI that surpasses human intelligence in all domains, including scientific creativity, wisdom, and social skills. ASI raises significant ethical and societal questions, especially in its potential impact on global governance, education systems, and humanity’s role in a highly automated world.
AI for Game-Based Learning (GBL)
O
N
C
E
P
T
s
AI-enhanced game-based learning platforms can simulate real-world international experiences, making them valuable tools for Collaborative Online International Learning (COIL). These AI-driven platforms can personalize educational games for international students, helping them understand diverse cultural contexts and global issues while enhancing their problem-solving skills in an interactive way.
AI Hallucinations
O
N
C
E
P
T
s
Instances where AI generates incorrect or nonsensical outputs that seem plausible but are factually inaccurate. This is a key concern in ensuring the ethical use of AI in academic settings, particularly in generative AI.
AI-Driven Career Coaching and Skills Mapping
c
O
N
C
E
P
T
s
AI tools that provide personalized career advice based on a student’s skills, interests, and experiences. These platforms can simulate job interviews, map skills against job market requirements, and recommend learning paths to bridge skills gaps.
AI for Cultural Adaptation and Support
O
N
C
E
P
T
s
AI tools like chatbots and virtual assistants help international students adapt to new cultural and academic environments. These tools provide real-time assistance on practical matters like housing, transportation, and local customs, easing the transition for students studying abroad.
AI for Monitoring and Evaluating Programs
O
N
C
E
P
T
s
AI’s ability to process large datasets from student feedback, program outcomes, and academic performance helps staff refine educational programs. In international education, this is essential for improving the effectiveness of exchange programs, employability initiatives, and other global engagements.
Bias Mitigation in AI Systems
O
N
C
E
P
T
s
As AI tools become integral to education, addressing biases in data and algorithms is essential to ensure fairness. In international higher education, bias mitigation ensures that AI tools serve students equitably, regardless of background.
Computer Vision
T
E
R
M
S
AI technology that enables computers to interpret and analyze visual information, such as images and videos. It is used in education for remote proctoring, automated grading, and even visual learning aids.
Collaborative Online International Learning (COIL)
O
N
C
E
P
T
s
A virtual exchange model that connects students and faculty across international institutions using AI-powered tools. COIL programs enable cross-border collaboration, enriching the academic experience for students who may not be able to study abroad physically.
Creative Pedagogy with AI
O
N
C
E
P
T
s
An educational approach that integrates AI tools to stimulate creativity among students and educators. In the context of international higher education, AI-enabled creative pedagogy can foster innovation by enabling learners to interact with AI in tasks such as multicultural projects, cross-border research, and global issue simulations. This fosters both creativity and collaboration on a global scale.
Deep Learning
T
E
R
M
S
An advanced form of machine learning using neural networks with multiple layers. It is particularly effective in processing vast datasets and performing tasks such as natural language processing (NLP), image recognition, and personalized education content delivery.
Data Privacy in AI
c
O
N
C
E
P
T
s
Concerns surrounding the collection, storage, and use of personal data by AI systems. In international education, ensuring ethical handling of student data is critical, especially when dealing with diverse data privacy regulations across different regions.
Digital Acculturation
O
N
C
E
P
T
s
The process by which individuals adapt to and integrate new cultural norms and practices through digital means. AI can play a significant role in helping international students adjust to new academic and cultural environments by offering personalized content and guidance.
Diffusion Models
T
E
R
M
S
A class of generative models used to create high-quality content such as images and videos. These models are especially relevant for generating dynamic visuals in creative education environments, including presentations, simulations, and immersive learning experiences.
Generative AI
T
E
R
M
S
A subset of AI that creates new content—such as text, images, music, or video—using deep learning models. Generative AI tools like OpenAI’s GPT-4 and DALL-E have transformed content creation in higher education by producing marketing materials, drafting academic content, and offering personalized feedback to students.
Generative AI for Recruitment Campaigns
O
N
C
E
P
T
s
AI’s role in automating personalized marketing campaigns targeted at prospective international students. Tools like ChatGPT help create tailored content that resonates with diverse audiences, enhancing international student recruitment efforts.
Human-AI Co-Creativity
O
N
C
E
P
T
s
The collaboration between humans and AI systems to foster creativity and innovation. In international higher education, AI tools can support students and educators in creative tasks such as research, content creation, and collaborative problem-solving. This concept encourages viewing AI not just as a tool for automation but as a partner in generating new ideas and facilitating cross-cultural and interdisciplinary learning.
Human-Centered AI in Higher Education
O
N
C
E
P
T
s
AI systems designed with the purpose of enhancing, rather than replacing, human roles in education. For the international higher education sector, it means leveraging AI to assist educators in administrative tasks, while focusing on maintaining teacher and student agency in decision-making. This concept is crucial for ensuring that AI tools empower rather than disempower students and faculty in global education settings.
Hybrid Intelligence
O
N
C
E
P
T
s
The integration of human and artificial intelligence to solve complex problems and enhance learning processes. In international higher education, hybrid intelligence can be leveraged to support global collaboration in research, where AI processes data and offers insights, while human researchers apply critical thinking and creativity to make nuanced decisions.
Machine Learning (ML)
T
E
R
M
S
A branch of AI that enables systems to learn from data and improve performance over time. ML is widely used in higher education for predictive analytics, student performance tracking, and adaptive learning systems.
MYCIN
T
E
R
M
S
An early AI system developed in the 1970s at Stanford University, designed to assist with medical diagnoses by simulating expert decision-making through a rule-based approach. MYCIN is often cited as one of the first practical applications of AI and paved the way for modern AI-driven decision-support systems.
Natural Language Processing (NLP)
T
E
R
M
S
A field of AI focused on the interaction between computers and humans using natural language. NLP powers chatbots, virtual assistants, and tools like ChatGPT that help streamline communication in student support, admissions, and learning environments.
Narrow AI (Weak AI)
T
E
R
M
S
AI systems designed to perform specific tasks or a limited range of functions. Unlike Artificial General Intelligence (AGI), narrow AI operates within a predefined scope and cannot generalize knowledge across different domains. Examples include language translation tools, chatbots, and recommendation systems commonly used in student support services and administrative functions.
Neural Networks
T
E
R
M
S
The core component of deep learning models, neural networks are computational frameworks that simulate the way neurons in the human brain function. They are widely used in AI applications such as speech recognition, language translation, and grading systems.
PLATO (Programmed Logic for Automatic Teaching Operations)
T
E
R
M
S
Developed at the University of Illinois, PLATO was one of the first systems to demonstrate the potential of AI in education by offering computer-assisted learning. PLATO laid the foundation for modern intelligent tutoring systems (ITS) and learning management systems.
